By Topic

Fault classification in gears using support vector machines (SVMs) and signal processing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Soleimani, A. ; Noise, Vibration, Acoust. (NVA) Res. Center, Univ. of Tehran, Tehran, Iran ; Mahjoob, M.J. ; Shariatpanahi, M.

This study presents a procedure for gear fault identification based on vibration signal processing techniques and support vector machines (SVMs). The required feature vector is extracted from vibration signals by time, frequency and time-frequency analysis. A feature selection technique based on Euclidian distance is utilized and five salient features are selected from the original feature set. These features are fed into the classification algorithm. Gear conditions considered were healthy, slightly worn, medium worn and broken-teeth gears. The output of classifier algorithm indicates the status of the gearbox by four labels. The results show that the developed SVM-based procedure is able to discriminate the faults clearly. The effectiveness of the feature selection method is demonstrated by experiments.

Published in:

Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on

Date of Conference:

2-4 Sept. 2009